code: def test_dict_spec_and_pre_processing(self):
      input_spec = {
          "a": tensor_spec.TensorSpec((32, 32, 3), tf.float32),
          "b": tensor_spec.TensorSpec((32, 32, 3), tf.float32),
      }
      network = encoding_network.EncodingNetwork(
          input_spec,
          preprocessing_layers={
              "a": tf.keras.Sequential(
                  [
                      tf.keras.layers.Dense(4, activation="tanh"),
                      tf.keras.layers.Flatten(),
                  ]
              ),
              "b": tf.keras.layers.Flatten(),
          },
          fc_layer_params=(),
          preprocessing_combiner=tf.keras.layers.Concatenate(axis=-1),
          activation_fn=tf.keras.activations.tanh,
      )
  
      sample_input = tensor_spec.sample_spec_nest(input_spec)
      output, _ = network(sample_input)
      # 6144 is the shape from a concat of flat (32, 32, 3) x2.
      self.assertEqual((7168,), output.shape)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.networks import encoding_network
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import test_utils
label_desc: Use image node_id 10 for calling the EncodingNetworkTest obj's underlying member method code with example usage: obj.test_dict_spec_and_pre_processing() without return types